In a perspective imaging system, a degradation of an image quality usually occurs. For example, an instability of a X-ray source or a detector will introduce a noise, which will affect a visual effects for an image, an observation of inspectors to an image content, and a discovery of the suspected area in the image. A degree for the noise in the imaging system can be estimated by using a certain evaluation algorithm, so as to monitor the stability of the imaging system in time.
In consideration of the fact that a non-degraded reference image can not be obtained in practical application, a method of evaluating the image quality without the reference image is more practical. The method of evaluating the image quality without the reference image may include a rule-based method, a machine learning based method, a probabilistic model based method, and the like. The essence of each method is to attempt to find a difference between high-quality images and degraded images statistically. For the rule-based method, it is difficult to devise a certain rule to distinguish different image qualities based on different features, due to the complexity of degeneration factors. For the machine learning based method, it is necessary to learn the images with different image qualities pre-marked by human observers. Thus, there is a heavy workload, and there may be differences between different marking people. For the probabilistic model based method, the statistical probability model of the image feature is established by only using some high-quality images. During the evaluation, the image quality can be obtained by calculating a probability of current image in the probability model. However, the probabilistic model based method needs to choose a certain probability mathematical model. When the model is too complex, it is difficult to perform parameter estimation, while it can not describe changes of image content effectively when the model is too simple. In addition, the learning of model parameters requires a large amount of data, and the iterations in the learning process also make the speed slower.